AI agents and customer support automation workflows

A look at how AI agents can impact customer support through the power of automation.

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Implementing AI in customer support

In the final tutorial of our Implementing Customer Support AI Assistants, you’ll learn more about AI agents and their impact on automating your customer support workflows.

As a reminder, AI Agents provide instant and accurate answers to customers directly in chat and other channel interfaces. These tools act as the first line of support triage, directly interfacing with customers to answer inquiries, before looping in human agents.

These workflows are not currently possible with ChatGPT or custom GPTs yet, so we’ll be reviewing AI customer support platforms (e.g. Intercom, Zendesk, and Kustomer) and AI chatbot builders (e.g. My Ask AI, Chatbase, and Zapier AI Chatbots) for this tutorial.

In the first section of this tutorial, we’ll provide an overview of AI agents before diving into some of the tools in the market.

Sections:

  1. Overview of AI agents
  2. Benefits and challenges of AI agents
  3. Reviewing tools in the market
  4. Recommendations for deploying an AI agent
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Overview of AI agents

Customer support AI agents serve various functions in improving customer experience, operational efficiency, and support quality. If you’ve ever visited a website and interacted with an automated chat window, you’ve most likely communicated with an AI agent.

These experiences and tools have come much farther than the pre-recorded robotic agents that you may have experienced when calling your bank or credit card company in the past.

💡 Tip: Intercom’s Fin chatbot, as shown in the above screenshot, is an example of a customer support AI agent. It can answer questions without human intervention and route more complex queries to human agents based on various business logic.

Here’s an overview of the key aspects of AI agents:

  • Automated responses: AI agents can automatically handle common inquiries and issues, providing instant responses to your customers. This reduces wait times and frees human agents to tackle more complex issues.
  • Integration and multichannel support: AI agents can integrate into various customer support channels like email, chat, social media, and phone. This can ensure a seamless experience for the customer, regardless of the communication medium.
  • Customer interaction analytics: AI tools analyze customer interactions to identify trends, predict customer needs, and provide insights for improving service quality. This data-driven approach helps in tailoring customer experiences and improving satisfaction.
  • Case routing and management: AI systems can intelligently route cases to the appropriate human agent based on the complexity and nature of the issue, ensuring that customers are quickly connected to the most qualified person to help them.
  • Learning and adaptation: Over time, AI agents learn from interactions and feedback to improve their accuracy and efficiency in handling inquiries. This continuous learning process helps in refining the responses and strategies used to manage customer interactions.

As you can see, AI agents offer a host of capabilities that can transform your customer support function for both your internal teams and your customers.

Benefits and challenges of AI agents

So what are the benefits and challenges of deploying AI agents for your business? Similar to the benefits we outlined at the beginning of this course, AI agents provide a host of advantages to customer service teams, including:

  • Increased efficiency and productivity: AI agents can handle multiple inquiries simultaneously, reducing wait times and increasing the volume of queries that can be processed in a given time frame. This helps customer support teams manage peak times more effectively without compromising service quality.
  • 24/7 availability: AI agents can operate around the clock, providing constant support to customers across different time zones. This ensures that help is always available, improving customer satisfaction and engagement.
  • Consistency in responses: AI agents provide standardized responses to frequently asked questions, ensuring that all customers receive consistent information and quality of service. This consistency helps in maintaining a professional and reliable brand image.
  • Personalization: Advanced AI agents are capable of personalizing interactions by accessing customer data, such as purchase history or previous queries. This tailored approach makes interactions more relevant and can enhance customer satisfaction and loyalty.

While customer support AI agents offer numerous benefits, there are challenges associated with their implementation and operation. Here are some of the key issues:

  • Limited understanding of complex queries: AI agents, particularly those based on simpler models, can struggle with understanding and processing complex or nuanced customer queries. They may fail to grasp the context or the specific needs of customers, leading to unsatisfactory or irrelevant responses.
  • Integration issues: Integrating AI into existing customer support systems can be challenging, especially if your business has legacy systems or a sprawl of customer support platforms.
  • Customer acceptance: Some customers prefer interacting with human agents and might be skeptical or unsatisfied with automated responses.
  • Privacy concerns: AI systems often need access to vast amounts of data to function effectively. Handling such data raises significant privacy concerns, requiring strict adherence to data protection regulations and standards to maintain customer trust.
  • Maintenance and training: AI systems require continuous training and updating to handle new types of queries and changes in customer service protocols. This ongoing maintenance can be resource-intensive.
  • Language and cultural nuances: AI agents may not fully capture the subtleties of different languages and cultural contexts, which can lead to misunderstandings or inappropriate responses when dealing with a global customer base.

Even with these challenges, customer support AI agents can play a crucial role in transforming support services into more proactive, predictive, and personalized experiences—when deployed effectively—significantly benefiting both your business and your customers.

Reviewing tools in the market

Now that you understand what AI agents are, and the benefits and challenges of incorporating them into your business, we can look at some of the tools in the market that offer AI agent capabilities.

💡 Tip: In this tutorial, we are focusing on AI agents in a web-based, chat experience format (chat, email, social media, etc.). There are other modalities of AI agents (e.g. voice), which we may explore in future tutorials, but those will not be discussed in this tutorial.

We find it helpful to bucket AI agent tools into two categories.

Ai-powered customer support platforms

These are more robust suites of tools that can do everything from deploying AI agents, hosting your internal and external knowledge hubs, routing customer inquiries, providing deep analytics, and more. The more prominent providers in this space include:

AI chatbot builders

Newer entrants in the space have started as chatbot-only offerings. These are not singularly-purpose-driven customer support tools but are highly configurable AI chatbots that can be embedded on existing websites or standalone pages and used as customer support AI tools.

💡 Tip: We are not recommending one of these solutions over the other; it's highly dependent on your business needs and budget. This non-exhaustive list is intended to help you get started in your evaluation.

If you already have a customer support function and team, large sets of documentation, and highly complex customer support business logic, we recommend going with an AI-powered customer support platform (e.g. Intercom, Zendesk). You might already be using one of these platforms, which makes enabling and integrating its AI agent features that much easier.

These platforms will be more expensive and complex, but you will get what you pay for here, where you’ll be able to deploy AI agents across a suite of channels, integrate them into existing workflows and tools, and onboard a large team.

However, if you’re just starting with developing a customer support function or your customer support channel is chat-only and on the simpler side, going with an AI chatbot builder could be a good place to start.

These platforms are much simpler and quicker to get set up, are typically lower cost, and work well with smaller teams with simpler customer support workflows.

In addition to the above, make sure to evaluate these tools on the below considerations as well to ensure you’re making a well-informed decision:

  • Cost: Consider setup costs, monthly fees, and any additional costs for training or additional services.
  • Support and community: A strong user community and good vendor support can be invaluable for troubleshooting and advice.
  • Compliance and security: Ensure the tool complies with relevant data protection regulations and offers robust security measures.

Recommendations for deploying an AI agent

In the last section of this tutorial, we’ll walk through how you can think about deploying your first AI agent into your customer support workflows. This can be helpful once you’ve selected an AI agent tool or even during your tool evaluation process.

Define objectives and requirements

Start by clearly defining what you want to achieve with an AI customer support agent. Are you aiming to reduce response times, handle high volumes of queries, or improve customer satisfaction? Set specific, measurable goals. Consider the types of queries you want the AI to handle initially. These should typically be high-volume, low-complexity inquiries.

💡 Tip: Look at the data from the AI analyst we developed in the previous tutorial for insights. This can help you identify your first set of use cases for your AI agent.

Pilot testing

Choose one communication channel to start with, such as live chat, which is one of the easier channels for AI integration and monitoring. Deploy the AI agent to handle a subset of predefined use cases. This controlled environment will allow you to:

  • Monitor the AI agent’s performance in real-time
  • Understand how customers respond to an AI agent versus human agents
  • Identify any integration or operational challenges

Close monitoring and human intervention

During the pilot phase, maintain close human oversight to intervene when the AI agent fails to resolve an issue satisfactorily. This hybrid approach not only ensures customer satisfaction but also provides valuable data to improve AI agent responses.

Gather feedback and analyze the data

Collect feedback from both customers and human support agents. Analyze the data to assess the AI agent’s impact on response times, customer satisfaction, and resolution rates. Look for patterns where the AI agent succeeds and where it falls short.

If you’re using an AI-powered customer support platform, this analytics tooling should be included, if you’re using an AI chatbot builder, you can use the AI Analyst we developed in the previous tutorial to crunch the numbers.

Iterate and optimize

Use the insights gained from the pilot test to make necessary adjustments. This could involve retraining the AI agent with more data, expanding the types of queries it handles, or tweaking the integration settings.

Gradual expansion

Once the AI agent proves effective on one channel, consider expanding its use to other channels and more complex queries. Each expansion can follow the same testing and evaluation steps to ensure consistency and quality.

Continuous improvement

AI tooling and customer expectations are constantly evolving, so you will need to continuously iterate on your AI agent. Stay updated on new features and enhancements in your chosen tool and regularly update your training data.

By starting small, monitoring closely, and scaling responsibly, you can effectively integrate an AI customer support agent into your business, enhancing your service quality and operational efficiency.

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